Abstract

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signal has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously increase estimates of short-range connectivity. Here I propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modelling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard image processing pipelines. With this method, I demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published, and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts, and can consequently remove a range of linear and non-linear, high and low frequency artifacts from fMRI time series. In conclusion, there is a real risk of motion-related bias in connectivity analysis of fMRI data, but this risk is generally manageable, by effective pre-processing strategies designed to attenuate synchronized signal transients induced by ``spiky’’ head movements.